Semantic communities Semantic communities are built upon what Mohammad Ali Nematbakhsh calls "machines first, humans later" (Aghaei, 2012). The current web is similar to a global filing system where humans are the dependent factor on whether or not information is found and then accessed. The semantic community makes data readable by machines as well as humans; thus, expanding the boundaries of the internet communities. As humans, we are bound by a variety of different limitations when searching and connecting on the web, but with Web 3.0, we are aided by the computer itself when discovering information. A semantic community worth focusing on is the professional job search community. There are many other sites on the web that take the "machine first, human later" theory. Job searching websites like Monster and Nashville Hires are two that work with this method in mind. The diagram below shows how the information stored on these sites works. Imagine yourself as an Employer for a company. Now, you could go online and manually hunt through postings of resumes listed on the web, but that would take time and money that could better be allocated elsewhere in the era of Web 3.0. The most efficient way of finding the perfect candidate for your position would be to access the inference engine (bottom right). What the job hunter is doing is uploading their resume to one of the professional websites where it is then stored (bottom left-providing/storing). This is where the semantic community kicks in. Based on your desires as an employer, the 'inference engine' or language searches the warehouse of stored resumes until the two different machines communicate effectively enough to find the perfect candidate. References: Aghaei, S. (2012). Evolution of the world wide web: from web 1.0 to web 4.0. International Journal of Web and Semantic Technology, 3 (1). Retrieved January 25, 2013 from https://elearn.mtsu.edu/d2l/lms/content/viewer/main_frame.d2l?ou=3909772&tId=25245135
Semantic communities are built upon what Mohammad Ali Nematbakhsh calls "machines first, humans later" (Aghaei, 2012). The current web is similar to a global filing system where humans are the dependent factor on whether or not information is found and then accessed. The semantic community makes data readable by machines as well as humans; thus, expanding the boundaries of the internet communities. As humans, we are bound by a variety of different limitations when searching and connecting on the web, but with Web 3.0, we are aided by the computer itself when discovering information.
A semantic community worth focusing on is the professional job search community. There are many other sites on the web that take the "machine first, human later" theory. Job searching websites like Monster and Nashville Hires are two that work with this method in mind.
The diagram below shows how the information stored on these sites works. Imagine yourself as an Employer for a company. Now, you could go online and manually hunt through postings of resumes listed on the web, but that would take time and money that could better be allocated elsewhere in the era of Web 3.0. The most efficient way of finding the perfect candidate for your position would be to access the inference engine (bottom right). What the job hunter is doing is uploading their resume to one of the professional websites where it is then stored (bottom left-providing/storing).
This is where the semantic community kicks in. Based on your desires as an employer, the 'inference engine' or language searches the warehouse of stored resumes until the two different machines communicate effectively enough to find the perfect candidate.
References:
Aghaei, S. (2012). Evolution of the world wide web: from web 1.0 to web 4.0. International Journal of Web and Semantic Technology, 3 (1). Retrieved January 25, 2013 from
https://elearn.mtsu.edu/d2l/lms/content/viewer/main_frame.d2l?ou=3909772&tId=25245135